.png){kind=icon}
返回
Confidence Aware Deep Learning Driven Wireless Resource Allocation in Shared Spectrum Bands
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3162829.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Deep learning (DL) driven proactive resource allocation (RA) is a promising approach for the efficient management of network resources. However, DL models typically have a limitation that they do not capture the uncertainty due to the arrival of new unseen samples with a distribution different than the data distribution available at DL model-training time, leading to wrong resource usage predictions. To address this, we propose a confidence aware DL solution for the robust and reliable predictions of wireless channel utilization (CU) in shared spectrum bands. We utilize an encoder-decoder based Bayesian DL model to generate prediction intervals which capture the uncertainties in wireless CU. We use the CU predictions to design a novel metric score which in turn is utilized to make an adaptive RA algorithm. We show that a DL model capturing uncertainty in CU can achieve higher data rates for a wireless network. Both DL driven predictions and RA models are tested using synthetic data as well as real CU data collected in the University of Oulu. Using analytical and simulations results, we also study the stability of the proposed RA algorithm and show that it converges to a Nash equilibrium (NE). Our results reveal that the proposed algorithm converges to an NE under 2N iterations where N is the number of network access points.
Keyword:
6G
Bayesian neural networks
channel utilization
distribution change detection
dynamic wireless networks
game theory
predictive uncertainty
proactive resource allocation
shared spectrum bands
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions5g/B5G移动和无线通信的机器学习: 潜力、限制和未来方向.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏A comparative study of three different forecasting methods for trial of labor after cesarean sectionOAAI
OAAI分享收藏Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges人工智能支持5g及以后的无线网络: 最新进展和未来挑战OAAI
OAAI分享收藏Deep-Learning-Aided Cross-Layer Resource Allocation of OFDMA/NOMA Video Communication Systems深度学习辅助的OFDMA/NOMA视频通信系统跨层资源分配IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏
查看更多内容